📚 Topic Summary
Algorithmic bias in AI happens when the data used to train an AI system reflects existing prejudices or stereotypes. This means the AI might make unfair or discriminatory decisions, even if it wasn't programmed to do so intentionally. For example, if an AI used for hiring is trained on data where most engineers are men, it might unfairly favor male candidates. It's crucial to identify and correct algorithmic bias to ensure AI is fair and equitable for everyone.
🧠 Part A: Vocabulary
Match the terms with their definitions:
| Term |
Definition |
| 1. Algorithm |
a. Unfairness in AI due to biased training data. |
| 2. Bias |
b. A set of rules a computer follows to solve a problem. |
| 3. Training Data |
c. Prejudice in favor of or against one thing, person, or group. |
| 4. Algorithmic Bias |
d. Information used to teach an AI how to make decisions. |
| 5. Artificial Intelligence |
e. The ability of a computer to perform tasks usually requiring human intelligence. |
Click to reveal the answers
1. b
2. c
3. d
4. a
5. e
📝 Part B: Fill in the Blanks
Complete the paragraph using the words provided: fairness, data, decisions, bias, AI.
Algorithmic _________ can occur in _________ systems when the training _________ contains prejudices. This can lead to the _________ making unfair _________. It's important to consider _________ when developing and using AI.
Click to reveal the answers
Algorithmic bias can occur in AI systems when the training data contains prejudices. This can lead to the AI making unfair decisions. It's important to consider fairness when developing and using AI.
🤔 Part C: Critical Thinking
Imagine an AI is used to screen applications for a scholarship. What steps could be taken to ensure the AI does not exhibit algorithmic bias?
Click to see a possible answer
To prevent algorithmic bias in the scholarship application screening AI, several steps can be taken. First, ensure the training data is diverse and representative of all potential applicants. Second, regularly audit the AI's decisions to identify and correct any patterns of bias. Third, involve diverse stakeholders in the development and testing of the AI to ensure different perspectives are considered. Finally, be transparent about how the AI is used and allow applicants to appeal decisions they believe are unfair.